What can go wrong when a framework like TotEMTM is not used?

As an example of how costly modelling can be when a framework like TotEMTM is not used, consider what happened to a major international subscription media company. They had been an early innovator in a rapidly growing market which then matured. They realised they were losing customers to competitors who had entered the market later and they needed to do something about it. Being a switched-on company they wanted to use their customer behaviour data to help them decide what to do, so they formulated as their business question, “How do we retain customers?”

With the aim of retaining customers, the company applied CRISP-DM and developed a propensity model to apply differential treatments to different customers depending on their different characteristics. At a practical level, they translated that into a set of different ways for the customer service team to respond to different customers who rang up to cancel their subscription.

The campaign was very successful in that many customers were saved from leaving. However, despite successful customer retention, the company’s profitability declined sharply. The problem was that they were getting the right answer to the wrong question.

Why TotEMTM is more effective than a modelling methodology such as CRISP-DM

TotEMTM asks more fundamental business questions before diving into data modelling. The business issue for this company was not just to retain customers, but also to retain the right customers and to help the others leave. Some customers are not profitable, but it can be hard to spot them when you have millions of customers – hence the value of modelling.

A single cycle of TotEM

Finding the right business question.

In this case the right business question derived from TotEMTM turned out to be, “What is the propensity of a customer to be cured of not being profitable?” This business question was identified through the business-level engagement of TotEMTM and it completely changed the subsequent modelling approach. The right answer was now being sought to the right question.

The subsequent modelling using our enhanced form of CRISP-DM unearthed a significant group of customers who were repeatedly threatening to leave and being given incentives to stay. This was what made them unprofitable: they were freeloaders. The new model took this behaviour into account and identified customers whose behaviour needed to be changed or else they should be encouraged to leave.

This is how Red Olive’s TotEMTM differs from using a pure modelling approach such as CRISP-DM. Modelling frameworks ensures the right answer is achieved, but working with Red Olive and using TotEMTM additionally ensures it is the right answer to the right question.